ArticlemedRxiv : the preprint server for health sciences2025
EPIDEMIOLOGY OF LEVODOPA-INDUCED DYSKINESIA: PREVALENCE AND ASSOCIATED CLINICAL FACTORS IN LATIN AMERICA.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Although levodopa remains the gold standard treatment for Parkinson's disease (PD), its chronic use is associated with levodopa-induced dyskinesia (LID), a motor complication that impacts prognosis, quality of life, and treatment costs. Most known LID-associated factors have been identified in European-descendant populations. Objectives: To describe the epidemiology of LID in Latin American and Caribbean (LATAM) countries and assess the relevance of known and novel LID-associated factors in this population. Methods: We conducted a cross-sectional study using data from the Latin American Research consortium on the Genetics of Parkinson's Disease (LARGE-PD). We included PD patients with information on LID status and levodopa use from eight LATAM countries. LID prevalence was calculated overall and by country. Countries were compared on demographic and clinical variables. Logistic regression was used to identify associations with LID. Results: A total of 3,695 PD patients (58.8% male) were included. Overall LID prevalence was 25.4% [95% CI: 24.06-26.87], ranging from 9.3% in Colombia to 45.1% in Puerto Rico. Prevalence increased progressively with longer disease duration. Country comparisons showed that not all known LID-associated factors explained prevalence differences. In logistic regression, fast disease progression was significantly associated with LID (OR: 1.55, 95%CI: 1.16-2.07), while sex was not (OR: 1.02, 95%CI: 0.87-1.18). Conclusions: This is the largest study on LID epidemiology in LATAM. While some known risk factors remain relevant, others, like sex, do not, underscoring the need for population-specific studies. Future work should integrate environmental, clinical, and genetic data to better understand LID mechanisms.
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